Genomic traits are commonly observed across cancer types, yet current pan-cancer analyses primarily focus on shared molecular features, often overlooking potential imaging characteristics across cancers. This retrospective study included 793 patients from the I-SPY1 breast cancer cohort (n = 145), Duke-UPenn glioblastoma (GBM) cohort (n = 452), and an external validation cohort (n = 196). We developed and validated multiparametric MRI-based radiomic and deep learning models to extract both cancer-type common (CTC) and cancer type-specific (CTS) features associated with the prognosis of both cancers. The biological relevance of the identified CTC features was investigated through pathway analysis. Seven CTC radiomic features were identified, demonstrating superior survival prediction compared to cancer type-specific (CTS) features, with AUCs of 0.876 for breast cancer and 0.732 for GBM. The deep feature model stratified patients into distinct survival groups (p = 0.00029 for breast cancer; p = 0.0019 for GBM), with CTC features contributing more than CTS features. Independent validation confirmed their robustness (AUC: 0.784). CTC-associated genes were enriched in key pathways, including focal adhesion, suggesting a role in breast cancer brain metastasis. Our study reveals pan-cancer imaging phenotypes that predict survival and provide biological insights, highlighting their potential in precision oncology.

Not all patients with advanced pancreatic cancer (PC) profit from 2nd-line chemotherapy. We evaluated predictive factors that are routinely collected during clinical care with the aim to support an informed and shared decision. In a prospective study across 35 German sites, 151 patients with PC previously treated with gemcitabine/nab-paclitaxel were enrolled and 146 patients received biweekly nanoliposomal irinotecan/5-fluorouracil/FA. We investigated whether time-to-treatment-failure of 1st-line (TTF1) predicts 2nd-line treatment outcome. Patients were stratified into three equal cohorts based on TTF1. Primary endpoint was TTF2, with secondary endpoints including overall survival (OS) and growth modulation index (GMI). Median TTF2 was 3.71 months (95% CI 2.50-4.11). Median OS was 7.72 months (95% CI 6.11-9.00). TTF1 did not predict TTF2 or OS (HR 0.93, 95% CI 0.58-1.47. Baseline parameters significantly associated with TTF2 and OS included neutrophil count, CRP levels, and liver metastases, whereas ECOG performance score (PS) was primarily associated with OS and to a lesser extent with TTF2. During treatment, patients with a CA 19-9 reduction of ≥25% had significantly improved TTF2 and OS (p < 0.001). TTF1 is not predictive of TTF2 or OS. Therefore, 2nd-line treatment should not be withheld irrespective of duration of TTF1. CA 19-9 dynamics can be used to predict further benefit to some extent. EudraCT: 2016-005147-17; ClinicalTrials.gov: NCT03468335.

Homologous recombination deficiency (HRD) has emerged as a functional biomarker reflecting genome-wide DNA repair defects and genomic instability. While the Cancer Genome Atlas (TCGA) molecular classification provides valuable prognostic guidance in endometrial cancer (EC), it lacks resolution for DNA repair competency and therapeutic responsiveness. This study aimed to investigate whether HRD subtyping could complement TCGA classification for improved prognostic stratification and therapeutic decision-making. A total of 142 EC patients were analysed using a next-generation sequencing panel and genomic scar-based HRD scoring (loss of heterozygosity, telomeric allelic imbalance, large-scale state transitions). Unsupervised clustering stratified patients into HRD-High, -Middle, and -Low groups. Maximally selected rank statistics were used to identify prognostic thresholds for HRD scores; the tumour-immune microenvironment was characterised by RNA-based immune gene expression profiling and multiplex immunohistochemistry. A support vector machine (SVM) model was developed for recurrence prediction. HRD subtyping identified distinct genomic, pathological, and immunological features. HRD-High tumours were associated with advanced FIGO stages, TP53 mutations, higher chromosomal instability, and elevated CD8⁺PD-1⁺ T-cell infiltration. HRD subtyping independently predicted disease-free survival and showed superior prognostic accuracy (C-index = 0.857) compared to TCGA subtyping (C-index = 0.751). Integrating HRD and TCGA classifiers further improved predictive performance (C-index = 0.903). An SVM model incorporating HRD score and immune features achieved an AUC of 0.733 for recurrence prediction. HRD subtyping refines risk stratification beyond traditional TCGA classification and identifies patients potentially responsive to immune checkpoint or DNA damage-targeted therapies. Integrating HRD-based genomic instability metrics with molecular and immune profiling supports precision oncology in endometrial cancer.

KRASG12C alterations are present in ~13% of lung adenocarcinomas. AZD4625 is a covalent small molecule inhibitor that selectively binds and inhibits GDP-KRASG12C, leading to reduced cell viability and protein signaling responsible for tumor survival in models with this gain-of-function alteration. We studied short-term changes in signaling and mechanisms of primary resistance to AZD4625 in twelve KRASG12C lung adenocarcinoma patient-derived xenografts (PDX) and six organoids derived from these twelve models. Sustained tumor regression in four (33%) PDXs was observed while the remaining eight models were intrinsically resistant to AZD4625. Organoid responses to AZD4625 were concordant with their derived PDXs. Acute AZD4625 exposure significantly decreased gene expression of the ERK1/2 negative regulator, DUSP6, in all models while protein MAPK and AKT/mTOR signals were downregulated more frequently in the AZD4625-sensitive than AZD4625-resistant cohorts. Analyzing PDX transcriptomes and proteomes identified mTOR signaling as a putative mechanism of primary resistance to AZD4625. Our findings confirm AZD4625 as a highly active KRASG12C inhibitor. This data also supports the use of PDX models in understanding resistance mechanisms that may be leveraged to develop more active combination therapies.

Circulating tumour cells (CTCs) are rare yet crucial biomarkers with significant prognostic potential across different cancer types. However, their role in high-grade serous ovarian cancer (HSGC) is not well defined. To capture the full spectrum of CTCs found in HGSC, we employed an EpCAM independent enrichment technique in patients with advanced HGSC and investigated the prognostic value and molecular signatures of these rare cells. CTC enumeration was performed in 43 newly diagnosed patients with HGSC using Parsortix® CTC enrichment and benchmarked against a metastatic breast cancer (MBC) cohort for which the device is FDA approved. CTCs were also isolated from the ovarian vein of patients with HGSC during primary cytoreductive surgery. CTCs were assessed as prognostic markers in patients with HGSC. FACS single cell sorting and scRNAseq was performed on CTCs isolated from the ovarian vein. CTCs isolated using Parsortix® enrichment in HGSC ranged between 1-22 cells/7.5 ml blood. Concordance was seen between Parsortix® enrichment and CellSearch® enumeration in patients with MBC (R2 = 0.8786). CTC clusters were isolated from the ovarian vein (P = 0.0195) and were cloaked in platelets/immune cells. Detection of CTCs in patients with HGSC was predictive of a poorer progression free survival (P = 0.0183). Patients with CTCs were found to have increased serum levels of CD73 (P = 0.0311). scRNAseq of CTCs isolated from the ovarian vein identified enrichment in genes associated with immune signalling. Peripheral CTCs isolated from patients with HGSC were predictors of a poor prognosis. The ovarian vein was found to be a rich source of disseminating CTC clusters in HGSC. Further studies are warranted to investigate the utility of CTCs as markers of neoadjuvant chemotherapy response as well as for longitudinal monitoring. Molecular analysis of CTCs in HGSCs reveals a potential role of the immune system in CTC-mediated haematogenous metastasis.

Circulating tumor DNA (ctDNA) is a promising biomarker for monitoring minimal residual disease (MRD), assessing disease status, and guiding treatment in diffuse large B-cell lymphoma (DLBCL). Current ctDNA assays rarely detect immunoglobulin (IG) fusions. This study evaluates a novel assay that simultaneously detects mutations, IG fusions, and IG V(D)J clonality in plasma and cerebrospinal fluid (CSF) to enhance molecular profiling and CNS monitoring. A prospective analysis was conducted in 57 DLBCL patients. Genomic alterations in plasma and CSF ctDNA were compared to those in tumor tissue using targeted next-generation sequencing (NGS). Mutations, IG fusions, and IG V(D)J clonality were detected in 100%, 72.2%, and 78.6% of plasma ctDNA samples, respectively. Plasma ctDNA also revealed additional mutations absent in tumor tissue, reflecting clonal heterogeneity. The incorporation of IG fusion detection enabled molecular subtyping without requiring FISH. In CSF, ctDNA analysis identified genomic alterations in 8 cases, whereas conventional imaging and cytology confirmed CNS involvement in only 3, demonstrating the superior sensitivity of ctDNA for CNS surveillance. This ctDNA assay offers a non-invasive, integrated approach for genomic profiling and disease monitoring in DLBCL, with improved sensitivity for CNS detection and potential to inform personalized treatment strategies.

Concurrent chemoradiotherapy (CCRT) is an important treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). There is still a lack of reliable means to predict efficacy, prognosis and hematologic toxicity. We analyzed 127 serum samples before CCRT and 93 serum samples after CCRT from 127 ESCC patients via metabolomics by GC-MS. Combined with Olink proteomics, we constructed models to predict response and survival through machine learning. Multiple linear regression was used to construct hematologic toxicity prediction models. In combination with the proteomics of ESCC, metabolic changes were studied. A prediction model for the efficacy to CCRT was established via serum metabolomics and proteomics (Train, CR/nCR = 28/50, AUC = 0.9848, 95% CI = 0.9639-1.0000; Test, CR/nCR = 17/15, AUC = 0.8854, 95% CI = 0.7800-0.9908). A survival prediction model was established (n = 109, C-index = 0.7640, 95% CI = 0.7140-0.8140). Linear models for predicting hematologic toxicity were constructed (n = 111, R > 0.7). L-serine is important for the prognosis of patients with ESCC treated with CCRT, and SHMT2 is a key protein in serine metabolism that affects the efficacy of CCRT. The combination of serum metabolomics with proteomics can effectively predict the prognosis and hematologic toxicity, which can provide important data for patients to choose treatment methods.

Socioeconomic factors are linked to cancer survival. While cancer stage and treatment mediate this association, the role of new morbidities after cancer treatment is less understood. We investigated educational disparities in cancer mortality and whether it is explained by differences in death from new morbidities. 85,849 cancer survivors with lung, breast, prostate, colorectal cancers were included. Follow-up for overall and cause-specific death began one year after diagnosis, lasting up to 16 years. Cox proportional hazard models estimated the association between education and death. Mediation analyses explored whether new morbidities mediated this association. Survivors with short education had higher mortality, particularly after breast and colon cancers, with a significant proportion of deaths due to new morbidities (breast cancer e.g. new primary cancers (HR 1.59, 95% CI:1.35,1.87), kidney and urinary problems (HR 4.16, 95% CI:1.24,13.97), and chronic respiratory issues (HR 2.99, 95% CI:1.86-4.79). Similar trends were observed in colon cancer survivors e.g. ischemic heart disease (HR 1.70, 95% CI:1.13,2.56) and heart failure (HR 3.53, 95% CI:1.35,9.19). Although some new morbidities were statistically significant mediators, they accounted for only a small proportion of the effects. Educational disparities persist in cancer survivorship, with new morbidities contributing to higher mortality rates among survivors with shorter education.